• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.2
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• pp.153-158
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• 2005

Recently, as the feasibility study shows that trans-Korea railway and trans-continental railway are advantageous, interest in high-speed railway system is increasing. Because railway vehicle is environment-friendly and safe compared with airplane and ship, its market-sharing increases gradually. KHST(Korean High Speed Train) has been developed by KRRI (Korea Railroad Research Institute) for last 6 years to satisfy the need. An electric railway system is composed of high-tech subsystems, among which main electric equipment such as transformers and converter are critical components determining the performance of rolling stock. We developed a measurement system for on-line test and evaluation of performances of KHST. The measurement system is composed of software part and hardware part. Perfect interface between multi-users is possible. A now method to measure temperature was applied to the measurement system. By using the system, fault diagnosis and performance evaluation of electric equipment in Korean High Speed Train was conducted during test running.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.1-7
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• 1999

In power transformers, vibration signals can occur at winding and core due to the change of current, voltage, and temperature and the deformation of winding and core. The deformation of winding and core occurs electromagnetic force induced by fault current in power systems. There firem the changes of vibration signals can be very different in normal or fault states of power transformers. We edtect and analyze the changes of vibration signals and use them as a tool for fault diagnosis of power transformers. This paper presents fault discriminating polliblility using the changes of fundamental waves and higher harmonics in power transformers. We showed the fault discriminating functions that are made at each case ; normal state and fault state. These functions are tested by the detected vibration signals, and we showed that the proposed method can discriminate the state of power transformers.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.463-464
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• 2011

Currently, different kinds of diagnosis and inspection technologies are applied to prevent the internal mechanical transformation of transformers. For example, examination of internal Partial Discharge of transformer, analysis of transformer oil gas, and measurement Frequency Response Analyzer(FRA) are used to diagnose defect. Especially, diagnosis technique through Frequency Response Analyzer(FRA) has been used and developed from 1960, when it was first introduced, till now to become an important tool to examine presence of defect and to prove quality of machines for the most electric machine producers electric power company in the world. However, diagnosis through FRA is still in introduction level in Korea and the application method for FRA is not established yet. For that reason, study about the application of domestic electric installation according to the FRA is needed. It is expected that the study play an important part in the prevention of defect due to the internal transformation of transformer by introducing measurement theory, providing measurement method, and analyzing application cases.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1007-1016
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• 2007

In power system, substation facilities have become too complex and larger according to an extended power system. Also, customers require the high quality of electrical power system. However, some facilities become old and often break down unexpectedly. The unexpected failure may cause a break in power system and loss of profits. Therefore it is important to prevent abrupt faults by monitoring the condition of power systems. Among the various power facilities, power transformers play an important role in the transmission and distribution systems. In this research, we develop intelligent diagnosis technique for predicting faults of power transformer by FCM(Fuzzy c-means) and Euclidean based distance measure. The proposed technique make it possible to measures the possibility and degree of aging as well as the faults occurred in transformer. To demonstrate the validity of proposed method, various experiments are performed and their results are presented.

• Transactions on Electrical and Electronic Materials
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• v.13 no.5
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• pp.229-232
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• 2012

DGA is one of the most recent techniques developed to diagnose the fault condition on oil filled insulation transformers. There are more than 6 known different methods of DGA fault interpretation technique and so there is the likelihood that they may vary in their interpretations. A series of combined interpretation methods that can determine the power transformer condition faults in one assessment is therefore needed. This paper presents a computer program- based system developed to combine four DGA assessment techniques; Rogers Ratio Method, IEC Basic Ratio Method, Duval Triangle method and Key Gas Method. An easy to use Graphic User Interface was designed to give a visual display of the four techniques. The result shows that this assessment method can increase the accuracy of DGA methods by up to 20% and the no prediction result had been reduced down to 0%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1869-1873
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• 2010

For reliable operation of oil-filled electrical equipment, monitoring and maintenance of insulating oil is essential. Dissolved gas analysis(DGA) is widely used for monitoring faults in high voltage electrical equipment in service. Therefore, oil analysis should be monitored regularly during its service life. KEPCO has investigated thousands of dissolved gas analysis data since 1985, and conducted studies on the relationship of gas in oil analysis and internal inspection results of transformer. As the results, KEPCO revised criteria for transformer diagnosis and has applied it since 2008. Almost of 100 cases of internal inspection results since 2001 have been investigated. This paper presents the correlation of the fault-identifying gases with faults found in actual transformers and how should we approach to internal inspection of transformer by dissolved gas analysis.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.12
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• pp.578-584
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• 2005

This paper describes development of management software for transformers based on artificial intelligent analysis technology of dissolved gases in oil. Fault interpretation using the artificial intelligent analysis is performed by the artificial neural network and a rule based on the analysis of dissolved gases. The used gases are acetylene($C_{2}H_{2}$), hydrogen($H_2$), ethylene($C_{2}H_{4}$), methane($CH_4$), ethane($C_{2}H_{6}$), carbon monoxide(CO) and carbon dioxide($CO_2$). This software is mainly composed of gases input, fault's causes, expected fault's phenomena in detail, the decision on maintenance as well as report and gas trend windows. It is indicated that this is very powerful software for the efficient management of oil-immersed transformers using data analysis of gas components.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.447-448
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• 2015

In order to looking for method of detecting inner fault of a 154kV GIT(Gas Insulated Transformer), it was considered that diagnosis partial discharge(PD) in UHF band and that analyze the ingredients of SF6 insulating gas. UHF PD diagnosis that is optimized to GIS was considered unsuitable through checking of inner part of a transformers which PD is detected excessively. The method analyzing the content of six kinds of gas(SOF2, SO2F2, etc)was decided through analysis of chemical degradation and combination process and discharge experiment. With the result applying this method to analyze the content of insulated gas of eighty five Gas Insulated Transformers.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.983-989
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• 2015

Power transformer is one of the major and key apparatus in electric power system. Monitoring and diagnosis of transformer fault is necessary for improving the life period of transformer. The failures caused by short circuits are one of the causes of transformer outages. The short circuit currents induce excessive forces in the transformer windings which result in winding deformation affecting the mechanical and electrical characteristics of the winding. In the present work, a transformer producing only the radial flux under short circuit is considered. The corresponding axial displacement profile of the windings is computed using Finite Element Method based transient structural analysis and thus obtained displacements are compared with the experimental result. The change in inter disc capacitance and mutual inductance of the deformed windings due to different short circuit currents are computed using Finite Element Method based field analyses and the corresponding Sweep Frequency Responses are computed using the modified electrical equivalent circuit. From the change in the first resonant frequency, the winding movement can be quantified which will be useful for estimating the mechanical withstand capability of the winding for different short circuit currents in the design stage itself.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.563-570
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• 2018

This paper proposes an efficient dissolved gas analysis(DGA) and classification method of an oil-filled transformer using machine learning algorithms to solve problems inherent in IEC 60599. In IEC 60599, a certain diagnosis criteria do not exist, and duplication area is existed. Thus, it is difficult to make a decision without any experts since the IEC 60599 standard can not support analysis and classification of gas date of a power transformer in that criteria. To address these issue. we propose a dissolved gas analysis(DGA) and classification method using a machine learning algorithm. We evaluate the performance of the proposed method using support vector machines with dissolved gas dataset extracted from a power transformer in the real industry. To validate the performance of the proposed method, we compares the proposed method with the IEC 60599 standard. Experimental results show that the proposed method outperforms the IEC 60599 in the classification accuracy.